Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data
Rear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition meth...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/9085238 |
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| _version_ | 1832550005668315136 |
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| author | Qingwen Xue Ke Wang Jian John Lu Yujie Liu |
| author_facet | Qingwen Xue Ke Wang Jian John Lu Yujie Liu |
| author_sort | Qingwen Xue |
| collection | DOAJ |
| description | Rear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition method based on vehicle trajectory data extracted from the surveillance video. First, three rear-end collision surrogates, Inversed Time to Collision (ITTC), Time-Headway (THW), and Modified Margin to Collision (MMTC), are selected to evaluate the collision risk level of vehicle trajectory for each driver. The driving style of each driver in training data is labelled based on their collision risk level using K-mean algorithm. Then, the driving style recognition model’s inputs are extracted from vehicle trajectory features, including acceleration, relative speed, and relative distance, using Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and statistical method to facilitate the driving style recognition. Finally, Supporting Vector Machine (SVM) is applied to recognize driving style based on the labelled data. The performance of Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) is also compared with SVM. The results show that SVM overperforms others with 91.7% accuracy with DWT feature extraction method. |
| format | Article |
| id | doaj-art-73935060cd8548b39e8646367415cb3b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-73935060cd8548b39e8646367415cb3b2025-02-03T06:07:57ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/90852389085238Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory DataQingwen Xue0Ke Wang1Jian John Lu2Yujie Liu3College of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaCollege of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaCollege of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaCollege of Transportation Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaRear-end collision crash is one of the most common accidents on the road. Accurate driving style recognition considering rear-end collision risk is crucial to design useful driver assistance systems and vehicle control systems. The purpose of this study is to develop a driving style recognition method based on vehicle trajectory data extracted from the surveillance video. First, three rear-end collision surrogates, Inversed Time to Collision (ITTC), Time-Headway (THW), and Modified Margin to Collision (MMTC), are selected to evaluate the collision risk level of vehicle trajectory for each driver. The driving style of each driver in training data is labelled based on their collision risk level using K-mean algorithm. Then, the driving style recognition model’s inputs are extracted from vehicle trajectory features, including acceleration, relative speed, and relative distance, using Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), and statistical method to facilitate the driving style recognition. Finally, Supporting Vector Machine (SVM) is applied to recognize driving style based on the labelled data. The performance of Random Forest (RF), K-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) is also compared with SVM. The results show that SVM overperforms others with 91.7% accuracy with DWT feature extraction method.http://dx.doi.org/10.1155/2019/9085238 |
| spellingShingle | Qingwen Xue Ke Wang Jian John Lu Yujie Liu Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data Journal of Advanced Transportation |
| title | Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data |
| title_full | Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data |
| title_fullStr | Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data |
| title_full_unstemmed | Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data |
| title_short | Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data |
| title_sort | rapid driving style recognition in car following using machine learning and vehicle trajectory data |
| url | http://dx.doi.org/10.1155/2019/9085238 |
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